The Rules Determination of Numerical Association Rule Mining Optimization by Using Combination of PSO and Cauchy Distribution

  • Imam TahyudinEmail author
  • Hidetaka Nambo
Conference paper
Part of the Lecture Notes on Multidisciplinary Industrial Engineering book series (LNMUINEN)


One of the optimization methods to solve the numerical association rule mining problem is particle swarm optimization (PSO). This method is popularly used in various fields such as in the job scheduling problem, evaluating stock market, inferring gen regulatory networks and numerical association rule mining optimization. The weakness of the PSO is often premature for searching the optimal solution because it traps in local optima when the best particle is being searched in every iteration. Combining the PSO with Cauchy distribution for numerical association rule mining problem (PARCD) is a solution because it is robust for finding the optimal solution in a large neighborhood. The important point in this proposed method is particle representation which to know the association between one attribute to another. Therefore, this study has the aim to determinate rules of numerical association rule mining and also to calculate the multi-objective function using combination of PSO and Cauchy distribution. The results show that all of them explain every attribute to formulate the rule well. In addition, the multi-objective function value of PARCD method generally produces results which better than the previous method, MOPAR.


PSO Cauchy distribution Numerical association rule mining Particle representation Multi-objective functions PARCD MOPAR 



This research supported by various parties. We would like to thank for scholarship program from Kanazawa University, Japan and Ministry of Research and Technology and Directorate of Higher Education (KEMENRISTEKDIKTI) and also STMIK AMIKOM Purwokerto, Indonesia. In addition, we thank for anonymous reviewers who gave input and correction for improving this research.


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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Artificial Intelligence Laboratory, Division of Electrical Engineering and Computer Science, Graduate School of Natural Science and TechnologyKanazawa UniversityKanazawaJapan
  2. 2.Department of Information SystemSTMIK AMIKOM PurwokertoPurwokertoIndonesia

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